TY - JOUR
T1 - Holistic Resource Allocation under Federated Scheduling for Parallel Real-time Tasks
AU - Nie, Lanshun
AU - Fan, Chenghao
AU - Lin, Shuang
AU - Zhang, Li
AU - Li, Yajuan
AU - Li, Jing
N1 - Funding Information:
Lanshun Nie and Chenghao Fan contributed equally to this research. This research was supported by the National Science Foundation (USA) under Grant Numbers CNS–1948457 and the National Natural Science Foundation of China under Grant No. U20A6003. Authors’ addresses: L. Nie, C. Fan, and S. Lin, Harbin Institute of Technology, 92 Xida St., Harbin, Heilongjiang 150001, CHN; L. Zhang, Amazon Web Services, 410 Terry Avenue North, Seattle, WA 98109-5210, USA; Y. Li and J. Li (corresponding author), New Jersey Institute of Technology, University Heights, Newark, NJ 07102, USA. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2022 Association for Computing Machinery. 1539-9087/2022/01-ART13 $15.00 https://doi.org/10.1145/3489467
Funding Information:
This research was supported by the National Science Foundation (USA) under Grant Numbers CNS?1948457 and the National Natural Science Foundation of China under Grant No. U20A6003.
Publisher Copyright:
© 2022 Association for Computing Machinery.
PY - 2022/1
Y1 - 2022/1
N2 - With the technology trend of hardware and workload consolidation for embedded systems and the rapid development of edge computing, there has been increasing interest in supporting parallel real-time tasks to better utilize the multi-core platforms while meeting the stringent real-time constraints. For parallel real-time tasks, the federated scheduling paradigm, which assigns each parallel task a set of dedicated cores, achieves good theoretical bounds by ensuring exclusive use of processing resources to reduce interferences. However, because cores share the last-level cache and memory bandwidth resources, in practice tasks may still interfere with each other despite executing on dedicated cores. Such resource interferences due to concurrent accesses can be even more severe for embedded platforms or edge servers, where the computing power and cache/memory space are limited. To tackle this issue, in this work, we present a holistic resource allocation framework for parallel real-time tasks under federated scheduling. Under our proposed framework, in addition to dedicated cores, each parallel task is also assigned with dedicated cache and memory bandwidth resources. Further, we propose a holistic resource allocation algorithm that well balances the allocation between different resources to achieve good schedulability. Additionally, we provide a full implementation of our framework by extending the federated scheduling system with Intel's Cache Allocation Technology and MemGuard. Finally, we demonstrate the practicality of our proposed framework via extensive numerical evaluations and empirical experiments using real benchmark programs.
AB - With the technology trend of hardware and workload consolidation for embedded systems and the rapid development of edge computing, there has been increasing interest in supporting parallel real-time tasks to better utilize the multi-core platforms while meeting the stringent real-time constraints. For parallel real-time tasks, the federated scheduling paradigm, which assigns each parallel task a set of dedicated cores, achieves good theoretical bounds by ensuring exclusive use of processing resources to reduce interferences. However, because cores share the last-level cache and memory bandwidth resources, in practice tasks may still interfere with each other despite executing on dedicated cores. Such resource interferences due to concurrent accesses can be even more severe for embedded platforms or edge servers, where the computing power and cache/memory space are limited. To tackle this issue, in this work, we present a holistic resource allocation framework for parallel real-time tasks under federated scheduling. Under our proposed framework, in addition to dedicated cores, each parallel task is also assigned with dedicated cache and memory bandwidth resources. Further, we propose a holistic resource allocation algorithm that well balances the allocation between different resources to achieve good schedulability. Additionally, we provide a full implementation of our framework by extending the federated scheduling system with Intel's Cache Allocation Technology and MemGuard. Finally, we demonstrate the practicality of our proposed framework via extensive numerical evaluations and empirical experiments using real benchmark programs.
KW - Parallel real-time systems
KW - federated scheduling
KW - resource partitioning
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U2 - 10.1145/3489467
DO - 10.1145/3489467
M3 - Article
AN - SCOPUS:85124790636
SN - 1539-9087
VL - 21
JO - Transactions on Embedded Computing Systems
JF - Transactions on Embedded Computing Systems
IS - 1
M1 - 13
ER -